OpenRaiser/Intern-Atlas huggingface dataset:
Approximately Dominating Sets in Elections
Venue: arXiv cs.GT 2025
Year: 2025

Condorcet's paradox is a fundamental result in social choice theory which states that there exist elections in which, no matter which candidate wins, a majority of voters prefer a different candidate. In fact, even if we can select any $k$ winners, there still may exist another candidate that would beat each of the winners in a majority vote. That is, elections may require arbitrarily large dominating sets. We show that approximately dominating sets of constant size always exist. In particular, for every $\varepsilon > 0$, every election (irrespective of the number of voters or candidates) can select $O(\frac{1}{\varepsilon ^2})$ winners such that no other candidate beats each of the winners by a margin of more than $\varepsilon$ fraction of voters. Our proof uses a simple probabilistic construction using samples from a maximal lottery, a well-studied distribution over candidates derived from the Nash equilibrium of a two-player game. In stark contrast to general approximate equilibria, which may require support logarithmic in the number of pure strategies, we show that maximal lotteries can be approximated with constant support size. These approximate maximal lotteries may be of independent interest.
GarmentX: Autoregressive Parametric Representations for High-Fidelity 3D Garment Generation
Venue: arXiv cs.CV 2025
Year: 2025

This work presents GarmentX, a novel framework for generating diverse, high-fidelity, and wearable 3D garments from a single input image. Traditional garment reconstruction methods directly predict 2D pattern edges and their connectivity, an overly unconstrained approach that often leads to severe self-intersections and physically implausible garment structures. In contrast, GarmentX introduces a structured and editable parametric representation compatible with GarmentCode, ensuring that the decoded sewing patterns always form valid, simulation-ready 3D garments while allowing for intuitive modifications of garment shape and style. To achieve this, we employ a masked autoregressive model that sequentially predicts garment parameters, leveraging autoregressive modeling for structured generation while mitigating inconsistencies in direct pattern prediction. Additionally, we introduce GarmentX dataset, a large-scale dataset of 378,682 garment parameter-image pairs, constructed through an automatic data generation pipeline that synthesizes diverse and high-quality garment images conditioned on parametric garment representations. Through integrating our method with GarmentX dataset, we achieve state-of-the-art performance in geometric fidelity and input image alignment, significantly outperforming prior approaches. We will release GarmentX dataset upon publication.
Undecidability of the Emptiness Problem of Deterministic Propositional While Programs with Graph Loop: Hypothesis Elimination Using Loops
Venue: arXiv cs.LO 2025
Year: 2025

We show that the emptiness (unsatisfiability) problem is undecidable and $\mathrmΠ^{0}_{1}$-complete for deterministic propositional while programs with (graph) loop. To this end, we introduce a hypothesis elimination using loops. Using this, we give reductions from the complement of the periodic domino problem. Moreover, as a corollary via hypothesis eliminations, we also show that the equational theory is $\mathrmΠ^{0}_{1}$-complete for the positive calculus of relations with transitive closure and difference. Additionally, we show that the emptiness problem is PSPACE-complete for the existential calculus of relations with transitive closure.
An Algebraic Approach to Asymmetric Delegation and Polymorphic Label Inference (Technical Report)
Venue: arXiv cs.PL 2025
Year: 2025

Language-based information flow control (IFC) enables reasoning about and enforcing security policies in decentralized applications. While information flow properties are relatively extensional and compositional, designing expressive systems that enforce such properties remains challenging. In particular, it can be difficult to use IFC labels to model certain security assumptions, such as semi-honest agents. Motivated by these modeling limitations, we study the algebraic semantics of lattice-based IFC label models, and propose a semantic framework that allows formalizing asymmetric delegation, which is partial delegation of confidentiality or integrity. Our framework supports downgrading of information and ensures their safety through nonmalleable information flow (NMIF). To demonstrate the practicality of our framework, we design and implement a novel algorithm that statically checks NMIF and a label inference procedure that efficiently supports bounded label polymorphism, allowing users to write code generic with respect to labels.
ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement
Venue: arXiv cs.SE 2025
Year: 2025

We present Agentic Retrieval-Augmented Code Synthesis (ARCS), a system that improves LLM-based code generation without fine-tuning. ARCS operates through a budgeted synthesize-execute-repair loop over a frozen model: it retrieves relevant code context before generation, proposes candidates, executes them against tests, and repairs based on execution feedback. This retrieval-before-generation design reduces hallucination and accelerates convergence. We formalize ARCS as a state-action process with provable guarantees on termination, monotonic improvement, and bounded cost. A tiered controller (Small/Medium/Large) trades latency for accuracy predictably. On HumanEval, ARCS achieves up to 87.2% pass@1 with Llama-3.1-405B, surpassing CodeAgent (82.3%) while using simpler control than tree-search methods. On TransCoder, it achieves >= 90% accuracy on most translation pairs. On a LANL scientific corpus, it improves CodeBLEU by +0.115 over baseline RAG. ARCS provides a practical, reproducible approach to reliable code synthesis using existing LLM checkpoints.
GaLore 2: Large-Scale LLM Pre-Training by Gradient Low-Rank Projection
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) have revolutionized natural language understanding and generation but face significant memory bottlenecks during training. GaLore, Gradient Low-Rank Projection, addresses this issue by leveraging the inherent low-rank structure of weight gradients, enabling substantial memory savings without sacrificing performance. Recent works further extend GaLore from various aspects, including low-bit quantization and higher-order tensor structures. However, there are several remaining challenges for GaLore, such as the computational overhead of SVD for subspace updates and the integration with state-of-the-art training parallelization strategies (e.g., FSDP). In this paper, we present GaLore 2, an efficient and scalable GaLore framework that addresses these challenges and incorporates recent advancements. In addition, we demonstrate the scalability of GaLore 2 by pre-training Llama 7B from scratch using up to 500 billion training tokens, highlighting its potential impact on real LLM pre-training scenarios.
PixelHacker: Image Inpainting with Structural and Semantic Consistency
Venue: arXiv cs.CV 2025
Year: 2025

Image inpainting is a fundamental research area between image editing and image generation. Recent state-of-the-art (SOTA) methods have explored novel attention mechanisms, lightweight architectures, and context-aware modeling, demonstrating impressive performance. However, they often struggle with complex structure (e.g., texture, shape, spatial relations) and semantics (e.g., color consistency, object restoration, and logical correctness), leading to artifacts and inappropriate generation. To address this challenge, we design a simple yet effective inpainting paradigm called latent categories guidance, and further propose a diffusion-based model named PixelHacker. Specifically, we first construct a large dataset containing 14 million image-mask pairs by annotating foreground and background (potential 116 and 21 categories, respectively). Then, we encode potential foreground and background representations separately through two fixed-size embeddings, and intermittently inject these features into the denoising process via linear attention. Finally, by pre-training on our dataset and fine-tuning on open-source benchmarks, we obtain PixelHacker. Extensive experiments show that PixelHacker comprehensively outperforms the SOTA on a wide range of datasets (Places2, CelebA-HQ, and FFHQ) and exhibits remarkable consistency in both structure and semantics. Project page at https://hustvl.github.io/PixelHacker.
Multidimensional precipitation index prediction based on CNN-LSTM hybrid framework
Venue: arXiv cs.LG 2025
Year: 2025

With the intensification of global climate change, accurate prediction of weather indicators is of great significance in disaster prevention and mitigation, agricultural production, and transportation. Precipitation, as one of the key meteorological indicators, plays a crucial role in water resource management, agricultural production, and urban flood control. This study proposes a multidimensional precipitation index prediction model based on a CNN- LSTM hybrid framework, aiming to improve the accuracy of precipitation forecasts. The dataset is sourced from Pune, Maharashtra, India, covering monthly mean precipitation data from 1972 to 2002. This dataset includes nearly 31 years (1972-2002) of monthly average precipitation, reflecting the long-term fluctuations and seasonal variations of precipitation in the region. By analyzing these time series data, the CNN-LSTM model effectively captures local features and long-term dependencies. Experimental results show that the model achieves a root mean square error (RMSE) of 6.752, which demonstrates a significant advantage over traditional time series prediction methods in terms of prediction accuracy and generalization ability. Furthermore, this study provides new research ideas for precipitation prediction. However, the model requires high computational resources when dealing with large-scale datasets, and its predictive ability for multidimensional precipitation data still needs improvement. Future research could extend the model to support and predict multidimensional precipitation data, thereby promoting the development of more accurate and efficient meteorological prediction technologies.
FT-MoE: Sustainable-learning Mixture of Experts for Fault-Tolerant Computing
Venue: arXiv cs.LG 2025
Year: 2025

Intelligent fault-tolerant (FT) computing has recently demonstrated significant advantages in predicting and diagnosing faults proactively, thereby ensuring reliable service delivery. However, due to the heterogeneity of fault knowledge, dynamic workloads, and limited data support, existing deep learning-based FT algorithms face challenges in fault detection quality and training efficiency. This is primarily because their homogenization of fault knowledge perception difficuties to fully capture diverse and complex fault patterns. To address these challenges, we propose FT-MoE, a sustainable-learning fault-tolerant computing framework based on a dual-path architecture for high-accuracy fault detection and classification. This model employs a mixture-of-experts (MoE) architecture, enabling different parameters to learn distinct fault knowledge. Additionally, we adopt a two-stage learning scheme that combines comprehensive offline training with continual online tuning, allowing the model to adaptively optimize its parameters in response to evolving real-time workloads. To facilitate realistic evaluation, we construct a new fault detection and classification dataset for edge networks, comprising 10,000 intervals with fine-grained resource features, surpassing existing datasets in both scale and granularity. Finally, we conduct extensive experiments on the FT benchmark to verify the effectiveness of FT-MoE. Results demonstrate that our model outperforms state-of-the-art methods.
APG-MOS: Auditory Perception Guided-MOS Predictor for Synthetic Speech
Venue: arXiv cs.SD 2025
Year: 2025

Automatic speech quality assessment aims to quantify subjective human perception of speech through computational models to reduce the need for labor-consuming manual evaluations. While models based on deep learning have achieved progress in predicting mean opinion scores (MOS) to assess synthetic speech, the neglect of fundamental auditory perception mechanisms limits consistency with human judgments. To address this issue, we propose an auditory perception guided-MOS prediction model (APG-MOS) that synergistically integrates auditory modeling with semantic analysis to enhance consistency with human judgments. Specifically, we first design a perceptual module, grounded in biological auditory mechanisms, to simulate cochlear functions, which encodes acoustic signals into biologically aligned electrochemical representations. Secondly, we propose a residual vector quantization (RVQ)-based semantic distortion modeling method to quantify the degradation of speech quality at the semantic level. Finally, we design a residual cross-attention architecture, coupled with a progressive learning strategy, to enable multimodal fusion of encoded electrochemical signals and semantic representations. Experiments demonstrate that APG-MOS achieves superior performance on two primary benchmarks. Our code and checkpoint will be available on a public repository upon publication.
LMME3DHF: Benchmarking and Evaluating Multimodal 3D Human Face Generation with LMMs
Venue: arXiv cs.CV 2025
Year: 2025

The rapid advancement in generative artificial intelligence have enabled the creation of 3D human faces (HFs) for applications including media production, virtual reality, security, healthcare, and game development, etc. However, assessing the quality and realism of these AI-generated 3D human faces remains a significant challenge due to the subjective nature of human perception and innate perceptual sensitivity to facial features. To this end, we conduct a comprehensive study on the quality assessment of AI-generated 3D human faces. We first introduce Gen3DHF, a large-scale benchmark comprising 2,000 videos of AI-Generated 3D Human Faces along with 4,000 Mean Opinion Scores (MOS) collected across two dimensions, i.e., quality and authenticity, 2,000 distortion-aware saliency maps and distortion descriptions. Based on Gen3DHF, we propose LMME3DHF, a Large Multimodal Model (LMM)-based metric for Evaluating 3DHF capable of quality and authenticity score prediction, distortion-aware visual question answering, and distortion-aware saliency prediction. Experimental results show that LMME3DHF achieves state-of-the-art performance, surpassing existing methods in both accurately predicting quality scores for AI-generated 3D human faces and effectively identifying distortion-aware salient regions and distortion types, while maintaining strong alignment with human perceptual judgments. Both the Gen3DHF database and the LMME3DHF will be released upon the publication.
Group Relative Knowledge Distillation: Learning from Teacher's Relational Inductive Bias
Venue: arXiv cs.LG 2025
Year: 2025

Knowledge distillation typically transfers knowledge from a teacher model to a student model by minimizing differences between their output distributions. However, existing distillation approaches largely focus on mimicking absolute probabilities and neglect the valuable relational inductive biases embedded in the teacher's relative predictions, leading to exposure bias. In this paper, we propose Group Relative Knowledge Distillation (GRKD), a novel framework that distills teacher knowledge by learning the relative ranking among classes, rather than directly fitting the absolute distribution. Specifically, we introduce a group relative loss that encourages the student model to preserve the pairwise preference orderings provided by the teacher's outputs. Extensive experiments on classification benchmarks demonstrate that GRKD achieves superior generalization compared to existing methods, especially in tasks requiring fine-grained class differentiation. Our method provides a new perspective on exploiting teacher knowledge, focusing on relational structure rather than absolute likelihood.
Hetu v2: A General and Scalable Deep Learning System with Hierarchical and Heterogeneous Single Program Multiple Data Annotations
Venue: arXiv cs.DC 2025
Year: 2025

The Single Program Multiple Data (SPMD) paradigm provides a unified abstraction to annotate various parallel dimensions in distributed deep learning (DL) training. With SPMD, users can write training programs from the viewpoint of a single device, and the system will automatically deduce the tensor sharding and communication patterns. However, with the recent development in large-scale DL models, distributed training exhibits spatial and temporal workload heterogeneity, arising from both device disparities (e.g., mixed hardware, failures) and data variations (e.g., uneven sequence lengths). Such heterogeneity violates SPMD's assumption of uniform workload partitioning, which restricts its ability to express and optimize heterogeneous parallel strategies effectively. To address this, we propose HSPMD within the Hetu v2 system to achieve general and scalable DL training. HSPMD extends SPMD's annotations to support asymmetric sharding and composes standard communication primitives for hierarchical communication, all while retaining the simplicity of a single-device declarative programming model. Leveraging HSPMD, Hetu handles spatial heterogeneity through progressive graph specialization, enabling device-specific execution logic, and addresses temporal heterogeneity via dynamic graph switching. Evaluations on heterogeneous clusters, elastic training, and mixed-length data scenarios show that HSPMD matches or outperforms specialized systems, providing a flexible and efficient solution for modern large-scale model training.
Token-Efficient Prompt Injection Attack: Provoking Cessation in LLM Reasoning via Adaptive Token Compression
Venue: arXiv cs.CR 2025
Year: 2025

While reasoning large language models (LLMs) demonstrate remarkable performance across various tasks, they also contain notable security vulnerabilities. Recent research has uncovered a "thinking-stopped" vulnerability in DeepSeek-R1, where model-generated reasoning tokens can forcibly interrupt the inference process, resulting in empty responses that compromise LLM-integrated applications. However, existing methods triggering this vulnerability require complex mathematical word problems with long prompts--even exceeding 5,000 tokens. To reduce the token cost and formally define this vulnerability, we propose a novel prompt injection attack named "Reasoning Interruption Attack", based on adaptive token compression. We demonstrate that simple standalone arithmetic tasks can effectively trigger this vulnerability, and the prompts based on such tasks exhibit simpler logical structures than mathematical word problems. We develop a systematic approach to efficiently collect attack prompts and an adaptive token compression framework that utilizes LLMs to automatically compress these prompts. Experiments show our compression framework significantly reduces prompt length while maintaining effective attack capabilities. We further investigate the attack's performance via output prefix and analyze the underlying causes of the vulnerability, providing valuable insights for improving security in reasoning LLMs.
SteelBlastQC: Shot-blasted Steel Surface Dataset with Interpretable Detection of Surface Defects
Venue: arXiv cs.CV 2025
Year: 2025

Automating the quality control of shot-blasted steel surfaces is crucial for improving manufacturing efficiency and consistency. This study presents a dataset of 1654 labeled RGB images (512x512) of steel surfaces, classified as either "ready for paint" or "needs shot-blasting." The dataset captures real-world surface defects, including discoloration, welding lines, scratches and corrosion, making it well-suited for training computer vision models. Additionally, three classification approaches were evaluated: Compact Convolutional Transformers (CCT), Support Vector Machines (SVM) with ResNet-50 feature extraction, and a Convolutional Autoencoder (CAE). The supervised methods (CCT and SVM) achieve 95% classification accuracy on the test set, with CCT leveraging transformer-based attention mechanisms and SVM offering a computationally efficient alternative. The CAE approach, while less effective, establishes a baseline for unsupervised quality control. We present interpretable decision-making by all three neural networks, allowing industry users to visually pinpoint problematic regions and understand the model's rationale. By releasing the dataset and baseline codes, this work aims to support further research in defect detection, advance the development of interpretable computer vision models for quality control, and encourage the adoption of automated inspection systems in industrial applications.
Conversations with AI Chatbots Increase Short-Term Vaccine Intentions But Do Not Outperform Standard Public Health Messaging
Venue: arXiv cs.CY 2025
Year: 2025

Large language model (LLM) based chatbots show promise in persuasive communication, but existing studies often rely on weak controls or focus on belief change rather than behavioral intentions or outcomes. This pre-registered multi-country (US, Canada, UK) randomized controlled trial involving 930 vaccine-hesitant parents evaluated brief (three-minute) multi-turn conversations with LLM-based chatbots against standard public health messaging approaches for increasing human papillomavirus (HPV) vaccine intentions for their children. Participants were randomly assigned to: (1) a weak control (no message), (2) a strong control reflecting the standard of care (reading official public health materials), or (3 and 4) one of two chatbot conditions. One chatbot was prompted to deliver short, conversational responses, while the other used the model's default output style (longer with bullet points). While chatbot interactions significantly increased self-reported vaccination intent (by 7.1-10.3 points on a 100-point scale) compared to no message, they did not outperform standard public health materials, with the conversational chatbot performing significantly worse. Additionally, while the short-term effects of chatbot interactions faded during a 15-day follow-up, the effects of public health material persisted through a 45-day follow-up relative to no message. These findings suggest that while LLMs can effectively shift vaccination intentions in the short-term, their incremental value over existing public health communications is questionable, offering a more tempered view of their persuasive capabilities and highlighting the importance of integrating AI-driven tools alongside, rather than replacing, current public health strategies.